# simnetpy
Python Package for the creation and analysis of similarity networks.
<!-- Project Organization
--------------------
.
├── AUTHORS.md
├── LICENSE
├── README.md
├── notebooks
└── src/simnet
├── clustering
├── datasets
├── graph
├── plotting
├── similarity
└── utils
-->
## Installation
Install latest release from PyPi with pip
```
pip install simnetpy
```
### Install from source
The source code for this project can be installed using `git` and `pip`.
Clone repository
```
git clone https://github.com/amarnane/simnetpy.git
cd simnetpy
pip install .
```
To remove the package simply use
```
pip uninstall simnetpy
```
### Developer Mode
To install in developer mode (have changes in source code update without reinstallation) add `-e` flag
```
pip install -e .
```
### Graph Tool
There is one dependency that cannot be installed through pip - `Graph-Tool`. This is a result of it's underlying `c++` dependencies.
The simplest method for python users is to make use of a conda environment, install this package using the commands above and install `graph-tool` using `conda-forge`
```
conda install -c conda-forge graph-tool
```
Note: this will not work on Windows. Alternative (conda independent) solutions can be found on the [Graph Tool Website](https://git.skewed.de/count0/graph-tool/-/wikis/installation-instructions#installing-via-conda)
# Using `simnetpy`
```Python
import simnet as sn
import numpy as np
# create mixed guassian data with 100 nodes, 2 dimensions and 3 equally sized clusters.
N = 100
sizes=np.array([34,33,33])
d = 2
dataset = sn.datasets.mixed_multi_guassian(len(sizes), d, N, sizes=sizes)
# calculate pairwise similarity
S = sn.pairwise_sim(dataset.X, metric='euclidean', norm=True)
# Create igraph Igraph from matrix
gg = sn.network_from_sim_mat(S, method='knn', K=10)
# print graph stats
print(gg.graph_stats())
# true cluster quality
cqual_ytrue = sn.clustering.cluster_quality(gg, dataset.y)
print(cqual_ytrue)
# cluster
ylabels = sn.clustering.spectral_clustering(gg, laplacian='lrw')
# cluster accuracy
cacc = sn.clustering.cluster_accuracy(dataset.y, ylabels)
print(cacc)
# predicted cluster quality
cqual = sn.clustering.cluster_quality(gg, ylabels)
print(cqual)
```
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"description": "# simnetpy\n\nPython Package for the creation and analysis of similarity networks.\n\n<!-- Project Organization\n--------------------\n\n .\n \u251c\u2500\u2500 AUTHORS.md\n \u251c\u2500\u2500 LICENSE\n \u251c\u2500\u2500 README.md\n \u251c\u2500\u2500 notebooks\n \u2514\u2500\u2500 src/simnet\n \u251c\u2500\u2500 clustering\n \u251c\u2500\u2500 datasets\n \u251c\u2500\u2500 graph\n \u251c\u2500\u2500 plotting\n \u251c\u2500\u2500 similarity\n \u2514\u2500\u2500 utils \n-->\n\n\n## Installation\nInstall latest release from PyPi with pip\n```\npip install simnetpy\n```\n\n### Install from source\nThe source code for this project can be installed using `git` and `pip`. \nClone repository \n```\ngit clone https://github.com/amarnane/simnetpy.git\ncd simnetpy\npip install .\n```\n\nTo remove the package simply use \n```\npip uninstall simnetpy\n```\n\n### Developer Mode\nTo install in developer mode (have changes in source code update without reinstallation) add `-e` flag\n```\npip install -e .\n```\n\n### Graph Tool\nThere is one dependency that cannot be installed through pip - `Graph-Tool`. This is a result of it's underlying `c++` dependencies.\nThe simplest method for python users is to make use of a conda environment, install this package using the commands above and install `graph-tool` using `conda-forge`\n```\nconda install -c conda-forge graph-tool\n```\nNote: this will not work on Windows. Alternative (conda independent) solutions can be found on the [Graph Tool Website](https://git.skewed.de/count0/graph-tool/-/wikis/installation-instructions#installing-via-conda)\n\n# Using `simnetpy`\n```Python\nimport simnet as sn\nimport numpy as np\n\n# create mixed guassian data with 100 nodes, 2 dimensions and 3 equally sized clusters.\nN = 100\nsizes=np.array([34,33,33])\nd = 2\ndataset = sn.datasets.mixed_multi_guassian(len(sizes), d, N, sizes=sizes)\n\n# calculate pairwise similarity\nS = sn.pairwise_sim(dataset.X, metric='euclidean', norm=True)\n\n# Create igraph Igraph from matrix\ngg = sn.network_from_sim_mat(S, method='knn', K=10)\n\n# print graph stats\nprint(gg.graph_stats())\n\n# true cluster quality\ncqual_ytrue = sn.clustering.cluster_quality(gg, dataset.y)\nprint(cqual_ytrue)\n\n# cluster\nylabels = sn.clustering.spectral_clustering(gg, laplacian='lrw')\n\n# cluster accuracy\ncacc = sn.clustering.cluster_accuracy(dataset.y, ylabels)\nprint(cacc)\n\n# predicted cluster quality\ncqual = sn.clustering.cluster_quality(gg, ylabels)\nprint(cqual)\n```\n\n",
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